Fig 1.
The first part of the analysis entails the estimation of biophysical model parameters from individual EEG data. We use a Markov Chain random walk to estimate biophysical parameters from subject-specific whole-brain power spectra. This is followed by subject-specific simulations using either corticothalamic or cortical plasticity. Potential links with available FDG-PET data are further analysed. Abbreviations: excitatory corticothalamic synaptic strengths (GESE), inhibitory corticothalamic synaptic strengths (GESRE), Excitatory cortical synaptic strengths (GEE), inhibitory cortical synaptic strengths (GEI), intrathalamic synaptic strengths (GSRS), synaptic decay and rise constants (α and β), corticothalamic time delay (t0), electroencephalography (EEG).
Fig 2.
Estimating corticothalamic model parameters in DoC.
Panel A shows the group-averaged empirical power spectra and model-estimated power spectra for the different groups alongside the goodness of fit of the estimation for the different groups. Panel B presents a graphical representation of the model parameters and their estimates across the different groups. Blue circles in the UWS group denote MCS* patients. Both excitatory and inhibitory corticothalamic synaptic strengths differ between MCS and UWS. Panel C illustrates the results of our classification analysis of MCS vs. UWS, showing the feature importance for each parameter (left), the receiver operating characteristic (ROC) curve (middle), and the surrogate distribution of AUC values (right). The genuine AUC value is depicted in green. The features that remained in the final model are depicted in pink (left panel), with corticothalamic excitatory GESE being the feature or parameter that contributes most strongly to the classification. Abbreviations: healthy controls (HC), minimal conscious state (MCS), unresponsive wakefulness syndrome (UWS), excitatory corticothalamic synaptic strengths (GESE), inhibitory corticothalamic synaptic strengths (GESRE), Excitatory cortical synaptic strengths (GEE), inhibitory cortical synaptic strengths (GEI), intrathalamic synaptic strengths (GSRS), synaptic decay and rise constants (α and β), corticothalamic time delay (t0). (FDR corrected).
Fig 3.
Modelling synaptic plasticity in DoC.
Panels A and B show simulated power spectra for individual subjects during excitatory corticothalamic (A) and excitatory cortical synaptic plasticity (B). Panel C shows the Kullback-Leibler (abbreviated to KL) divergence during either cortical plasticity (D) or corticothalamic plasticity (E). Simulated power spectra were assessed for their similarity (low KL value) with those from healthy control subjects, with significant differences denoted by ** for p < 0.001 and* for p < 0.05. Panel D shows the group-averaged power spectra during the course of corticothalamic plasticity for the UWS and MCS groups. The same group-averaged power spectra are illustrated for different etiologies (E). Abbreviations: healthy controls (HC), minimal conscious state (MCS), unresponsive wakefulness syndrome (UWS), excitatory corticothalamic synaptic strengths (GESE), inhibitory corticothalamic synaptic strengths (GESRE), Excitatory cortical synaptic strengths (GEE), inhibitory cortical synaptic strengths (GEI), intrathalamic synaptic strengths (GSRS), synaptic decay and rise constants (α and β), corticothalamic time delay (t0).
Fig 4.
The relationship between modelled synaptic plasticity and FDG-PET findings in DoC.
Panel A shows scatter plots and correlations between the peak frequencies of empirical EEG data with the metabolic index from FDG-PET. Panel B shows the same for simulated data. Panels C and D show scatter plots, and correlations between the aperiodic exponent and metabolic index. Significant correlations were especially observed for the modelled data. Note that the x-coordinates of panel A and B differ because fewer patients exhibited identifiable spectral peaks (particularly in UWS) in the empirical data, resulting in less peak-frequency data than aperiodic data.